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.0em plus.0ex minus.2ex-.5emImplementation Details.

We choose Mask R-CNN [1]} with feature pyramid network [2]} as our detector head throughout the paper. The head design follows [3]}, [4]}. We train the model for 46.1k iterations with 1024x1024 image size, large scale jittering [3]}, batch size 256, weight decay 1e-4, momentum 0.9, and an initial learning rate 0.36. For the score combination, we use \(\alpha =0.35\)  and \(\beta =0.65\)  in equation REF . We use a maximum of 300 detections per image, and set temperature \(T=0.01\)  in equation REF . We use CLIP [6]} prompt templates and take the average text embeddings of each category. Please refer to Appendix  for a full list of hyper-parameter configurations.
